基于SK-MOMEDA的滚动轴承微弱故障特征提取

Feature Extraction of Weak Fault for Rolling Bearing based on Spectral Kurtosis and MOMEDA

针对滚动轴承早期周期性瞬态冲击不明显及谱峭度在低信噪比情况下分析效果差的问题,提出多点优化最小熵解卷积(Multipoint optimal minimum entropy deconvolution adjusted,MOMEDA)和谱峭度相结合的轴承微弱故障特征提取方法。首先,采用MOMEDA作为前置滤波器对含有强噪声的微弱故障冲击信号进行降噪,突显信号中的周期性冲击性成分;然后,通过谱峭度分析,以最佳中心频率和带宽对降噪的信号进行带通滤波;最后,对滤波后的信号进行Hilbert包络谱分析,便可以准确地获得轴承信号的故障特征频率。仿真信号和实验分析结果表明,该方法可有效增强振动信号的周期性瞬态冲击特征,提取出滚动轴承早期微弱故障特征。

Aiming at the problem that the early periodic transient impulse of rolling bearings is not obvious and the spectral kurtosis is poorly analyzed under low signal-to-noise ratio,a method of extracting the weak fault features of rolling bearing based on the combination of multipoint optimal minimum entropy deconvolution adjusted(MOMEDA)and spectral kurtosis is proposed.Firstly,MOMEDA is used as the prefilter to reduce the noise of weak fault impulse signal with strong noise and highlight the periodic impulse component in the signal.Then,through spectral kurtosis analysis,the denoised signal is filtered under the optimal center frequency and bandwidth.Finally,the fault characteristic frequency of bearing signal can be accurately obtained by Hilbert envelope spectrum analysis of filtered signal.The simulation and experimental results show that the method can effectively enhance the periodic transient impulse characteristics of vibration signals and extract the early weak fault characteristics of rolling bearing.